Method and apparatus for an adaptive filter architecture

ABSTRACT

A system that incorporates teachings of the subject disclosure may include, for example, a method for identifying a spectral region in a radio frequency spectrum, determining a signal strength of the spectral region, determining a correlation factor by correlating the signal strength of the spectral region, detecting according to the correlation factor interference in the spectral region, generating coefficient data to substantially suppress the interference in the spectral region, configuring a filter according to the coefficient data to substantially suppress the interference in the spectral region and produce a digital filtered signal, and transmitting the digital filtered signal to a base station. Other embodiments are disclosed.

PRIOR APPLICATION

This application is a continuation of U.S. patent application Ser. No.13/617,754 filed Sep. 14, 2012, which is a continuation of U.S. patentapplication Ser. No. 12/268,996 filed Nov. 11, 2008 (issued as U.S. Pat.No. 8,385,483), the disclosures of all of which are incorporated hereinby reference in their entirety.

FIELD OF THE DISCLOSURE

The subject disclosure is directed to a method and apparatus for anadaptive filter architecture.

BACKGROUND

The increased presence of wireless-based communication systems hasspurred substantial growth in the voice and data services available tocustomers. Wireless networks are now frequently installed in place oftraditional wired networks in office as well as home environments, andin local as well as large area networks. Typically, these networks havea base station that is allocated a radio frequency (RF) spectrum whichit divides into different channel frequencies that are then used tocommunicate with multiple remote (often mobile) communication devices.In a cellular system, the base station may be a cellular base stationand the remote devices would then be mobile handset communicators, likecellular phones, walkie-talkies, personal data assistants, etc. In alocal area network, the base station may be a wireless router, such asone compliant with one or more of the various IEEE 802.11 standards, andthe remote devices may be a desktop or laptop computer, wirelessprinter, another wireless node, etc. In any event, over time as thenumber of remote devices increases, the allocated spectrum for eachcommunication system has become increasingly more crowded and theavailable channel frequencies more scarce.

Whereas traditional network solutions relied upon a top down approach,where the available frequency spectrum bandwidth was first identifiedand then channelized, more recently some have proposed bottom upapproaches such as cognitive radios that proactively mine for “holes” inan available spectrum. Cognitive radios are, generally speaking,wireless communication devices that have transmission and receptioncharacteristics that can change based on a measure of the RF environmentof the device. A cognitive radio may scan a large frequency spectrum todetermine what frequency bands are not in use, and then set upcommunications to transmit over only those identified, unused frequencybands. In other applications, cognitive radio operation may be based onenvironmental data such as operational rules for the network, userbehavior data, user subscriber priority information, etc. Cognitiveradio techniques can be used in remote stations or base stations, andgenerally differ from intelligent antenna systems (e.g., multiple inputmultiple output MIMO devices) which rely upon beamforming to avoidinterference. For cognitive radios, accurate analysis of a spectralregion is important to identify available bands.

Analyzing spectral regions is difficult in general; and this difficultycan vary depending on the type of wireless communication networkinvolved. Wireless systems are often classified according to theirmodulation scheme, such as Time Division Multiple Access System (TDMA),Code Division Multiple Access (CDMA), etc. CDMA is a type of DirectSequence Spread Spectrum (DSSS) modulation scheme where channels aredefined by complementary, orthogonal or pseudo-random spreadingsequences or codes, with each user assigned a unique spreading sequencethat has a frequency much higher than that of the user's data signal.DSSS signals have spectral characteristics of bandwidth limited whitenoise in the RF spectrum. A typical DSSS signal is likely to have one ormore interference signals present, e.g., multipath, co-channel, etc. Thetask of identifying interference in a DSSS signal represents a classicdetection-of-signals-in-noise problem, where the “noise” that needs tobe detected is in fact a signal in a spectrum whose characteristics aresimilar to white noise. In other words, the white noise is the signalthat needs to be preserved, and the interference signal is undesired.

Cognitive radios typically employ modulations schemes such as OrthogonalFrequency-Division Multiple Access (OFDMA), which is popular forwideband digital communication and generally considered more robust thanCDMA in avoiding co-channel interference. Proper analysis of thefrequency spectrum is still difficult even in OFDMA-based system,because the conventional cognitive radios apply brute force algorithmsto sense and manage a spectral range. For example, to save time, systemstypically block out large portions of a spectral range if interferenceis detected there. The systems are based on avoidance algorithms.However, given the rapid growth in wireless communication systems, manyof which overlap in coverage area, these avoidance algorithms “lose” toomuch available bandwidth to make cognitive radios practical in allsituations.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an example illustration of a communication system;

FIG. 2 is an example illustration of frequency spectrums of a DSSSsignal and a narrowband digital carrier signal;

FIG. 3 is an example adaptive front-end controller used in a cognitiveradio.

FIG. 4A is an example illustration of frequency usage in a wirelessnetwork;

FIG. 4B is an example illustration of interference in an availablefrequency band;

FIG. 4C is an example illustration of an RF signal spectrum containing afirst wireless communication band according to a first wirelesscommunication standard and a second wireless communication bandaccording to second wireless communication standard;

FIG. 5 is an example system level block diagram of an interferencedetection system;

FIG. 6 is an example signal processing module;

FIG. 7 is an exemplary illustration of an interference detectionprogram; and

FIG. 8 is an exemplary illustration of a high strength channel detectionprogram used by the interference detection program of FIG. 4

DETAILED DESCRIPTION

Various examples are detailed hereinafter for an adaptive, digitalarchitectures that can be used in radio-frequency (RF) bandpass,bandstop (notch), and other filter applications. The techniques allowfor the design of adaptable wireless communication devices with improvedperformance through intelligent RF signal reception that may quicklyidentify and correct for signal interference, improve frequency channelsignal-to-noise ratios, collaboratively tune a receiver to optimumfrequency channels, more accurately estimate signal strength, or performother adaptive signal processing. In some applications discussed below,the techniques are implemented in a cognitive radio wireless systemcapable of identifying available frequency bands within a spectral rangeand then communicating exclusively within those bands, so as to avoidinterference between remote devices or between entire wireless systems.

By implementing the techniques in digital configurations as discussed inexamples herein, wireless devices can be formed having any number ofcomplex configurations of bandpass and bandstop (notch) filters,arranged in parallel or cascaded for sequential operation, e.g., havingindividual or groups of filters in series. Each of the filters may becontrolled by generating coefficient data to set not only the frequencyof the filter but also its bandwidth of operation. This allows for fullyadaptive filters, for example, where depending on the RF environmentdetected by the wireless device, an adaptive filter may be changed froma bandpass to a bandstop (notch) filter and a bank of such filters maybe modified from one configuration to another.

In many of the examples to follow, the techniques are described in termsof an adaptive front end controller for a wireless communication device,although it will be appreciated that these techniques may be implementedelsewhere within a wireless device as desired.

FIG. 1 illustrates an example telecommunication system 10 employing anadaptive digital filter apparatus. A plurality of remote units 12, 13A,13B, 13C, and 13D, in this case mobile units, communicate through one oftwo base stations 14 and 16, interfaced together through a switchingstation 18. The illustrated configuration may represent a peer-to-peertopology in which remote units communicate directly with one anotherwithout the need of the base station as a central host, or (as describedbelow) an infrastructure topology in which the base station routes alldata communications. The system 10 may represent a short range or longrange network. Any of the remote units 12 and 13A-13D may be a portabledigital assistant (PDA), cellular phone, vehicle, media player, laptopcomputer, wireless supported desktop computer, gaming system, wirelessnetworking device such as a router, switch, etc., or any other portablecomputing device. The base stations 14, 16 and the switching station 18may be collectively referred to as network infrastructure.

Each base station 14, 16 includes an adaptive digital filter apparatusfor intelligently analyzing an incoming RF signal (e.g., spectral regionor a wideband communication signal). In this manner, the base stations14 and 16 are considered cognitive wireless devices. However, any of themobile units 12, 13A, 13B, 13C, and 13D and/or two base stations 14 and16 may be designed as a cognitive wireless device, e.g., one capable ofadaptively controlling transmission bands by mining for availablefrequencies in a particular RF spectrum.

During data communications, the mobile units 12, 13A, 13B, 13C, and 13Dmay exchange voice, data or other information with one of the basestations 14, 16, each of which may be connected to a conventionallandline telephone network, another wireless cellular network, or otherwired or wireless data network, e.g., any computer- or server-basednetwork. As an example, information, such as voice information,transferred from the mobile unit 12 to one of the base stations 14, 16may be coupled from the base station to a telephone network to therebyconnect the mobile unit 12 with a land line telephone so that the landline telephone may receive the voice information. Conversely,information such as voice information may be transferred from a landline telephone to one of the base stations 14, 16, wherein the basestation in turn transfers the information to the mobile unit 12.

The mobile units 12, 13A, 13B, 13C, and 13D and the base stations 14, 16may exchange information in a digital format and operate undernarrowband or wideband communication protocols. For example, the mobileunit 12 may be a narrowband digital unit communicating with the basestation 14 as a narrowband base station using a narrowband communicationprotocol such as a Groupe Special Mobile (GSM) cellular network, alsoknown as a 2G cellular communication network, with implementations knownas General Packet Radio Service (GPRS), Enhanced Data rates for GSMEvolution (EDGE) and Circuit Switched Data (CSD) that each have theirown corresponding encoding schemes. The mobile units 13A, 13B, 13C, and13D may be wideband digital units that communicate with the base station16 as a wideband base station and using a wideband communicationprotocol such as a DSSS signal-based protocol like CDMA or UniversalMobile Telecommunications System (UMTS), also known as a 3G cellularnetwork. CDMA digital communication takes place using spread spectrumtechniques that broadcast signals having wide bandwidths, such as, forexample, 1.2288 megahertz (MHz) bandwidths. Similarly, UMTScommunication takes place using bandwidths that may range from 15-20MHz, for example. Generally, a channel having a bandwidth that issubstantially smaller than that of the wideband communication signal isreferred to as a narrowband channel, which in this application alsorefers to narrowband sub-bands which are used depending on the codingscheme.

Other examples of wideband communication protocols may be OFDMA systemsthat have spectral regions formed of sub-bands with varying widths,e.g., 1.25 MHz, 5 MHz, 10 MHz, or 20 MHz. The OFDMA systems may be usedin applications such as cognitive radios communication in a widebandnetwork.

The optional switching station 18 is generally responsible forcoordinating the activities of the base stations 14, 16 to ensure thatthe mobile units 12, 13A, 13B, 13C, and 13D are constantly incommunication with the base station 14, 16 or with some other basestations that are geographically dispersed. For example, the switchingstation 18 may coordinate communication handoffs of the mobile unit 12between the base stations 14 and another base station as the mobile unit12 roams between geographical areas that are covered by the two basestations.

Each base station 14, 16 has an adaptive front-end controller 20 that,as discussed further below, may contain a number of adaptive digitalfilters configurable into either a bandpass or bandstop configuration tomodify the incoming and outgoing RF signals for the respective basestation. The adaptive front-end controller 20 may perform any of anumber of different intelligent functions. In some examples, thecontroller 20 operates as a high performance interference filterdetecting interference in a spectral region and/or narrowband channeland properly tuning one or more digital filters to remove suchinterference.

FIG. 2 illustrates a typical frequency bandwidth of a telecommunicationsystem. In particular, FIG. 2 illustrates a frequency spectrum 50 of a1.288 MHz DSSS system that may used by the digital mobile units 13A,13B, 13C, and 13D to communicate with the base station 16, and a 200 kHzfrequency spectrum 52 used by the module unit 12 using a narrowbanddigital communication system to communicate with the base station 14.

As would be understood, the digital signal shown in 52 may interferewith the frequency spectrum 50. Therefore, the adaptive front-endcontroller 20 in the base station 16 contains adaptive digital filtersthat are digitally tuned to remove the interference caused by the 200kHz signal 52 from the DSSS signal 50, for example, by applying atransfer function given by:

${\Phi (f)} = \frac{{{S(f)}}^{2}}{{{S(f)}}^{2} + {{N(f)}}^{2}}$

where |S(f)₂| is the power spectral density (PSD) of the desired signaland |N(f)₂| is an estimate the PSD of the interference (noise) signal.If the nature of the interfering signal (noise term N) is assumed to bethat given by the interference signal 52, the shape of the filter may begiven, at least theoretically, by the notch frequency spectrum 54illustrated in FIG. 2. As discussed further below, with the adaptivefront-end controllers discussed herein not only can a notch (orbandstop) filter be tuned to the particular frequency corresponding tothe interference frequency channel 52, the bandwidth of the notchfrequency spectrum 54 can be adjusted to remove interface bands of anysize. Herein, any type of interference or noise in an RF signal may beconsidered a type of RF characteristics that the front-end controllerseeks to identify and filter before the underlying wireless device RFreceiver receives that RF signal. RF characteristic also refers to anydata signal that is to be identified a device, either for filtering outof an incoming RF signal or passing with an incoming RF signal, such aswhen the RF characteristic is a desired data signal.

The adaptive front-end controller 20 of FIG. 1 may contain a pluralityof adaptive filters which would allow the base stations to filtermultiple interference signals at the same time during a single clockcycle of operation.

The adaptive front-end controller 20 is described as identifying andremoving interference. However, the adaptive front-end controller canprovide a number of different functions depending on the application andwireless device. FIG. 3 illustrates another example application of anadaptive front-end controller used in a wireless device 60, which may bea cellular phone, cognitive radio, or other wireless device. The radio60 has two stages, a receiver stage 62 and a transmitter stage 64, eachcoupled to an antenna assembly 66, which may itself comprise one of moreantennas for the radio 60. The radio 60 has a first stage coupled to theantenna assembly 66 and includes an adaptive front-end controller 68that receives the input RF signal from the antenna and performs adaptivesignal processing on that RF signal before providing the modified RFsignal to an analog-to-digital converter 70, which then passes theadapted RF signal to a digital RF tuner 72.

The adaptive front-end controller 68 includes two RF signal samplers 74,76 connected between an RF adaptive filter stage 78 that is controlledby controller 80. The adaptive filter stage 78 may have a plurality oftunable digital filters that can sample an incoming signal andselectively provided bandpass or bandstop signal shaping of thatincoming signal, whether an entire wideband communication signal or anarrowband signal or various combinations of both. A controller 80 iscoupled to the samplers 74, 76 and filter stage 78 and serves as an RFlink adapter that along with the sampler 74 monitors the input RF signalfrom the antenna and determines various RF signal characteristics suchas the interferences and noise within the RF signal. The controller 80is configured to execute any number of a variety of signal processingalgorithms to analyze the received RF signal, and determine an optimalfilter state for the filter stage 78.

By providing tuning coefficient data to the filter stage 78, theadaptive front end controller 68 acts to pre-filter the received RFsignal before that signal is sent to the RF tuner 72 (e.g., a radiotuner), which analyzes the filtered RF signal and in the case of acognitive filter identifies available frequency bands in the filteredwideband communication (RF) signal, for tuning to a desired frequencyband (or channel). Either way, after filtering, the tuner 72 may thenperform its standard channel demodulation, data analysis, and localbroadcasting functions. The RF tuner 72 may be considered the receiverside of an overall radio tuner, while RF tuner 72′ may be considered thetransmitter side of the same radio tuner.

Prior to sending the filtered RF signal (e.g., filter widebandcommunication signal), the sampler 76 may provide an indication of thefiltered RF signal to the controller 80 in a feedback manner for furtheradjusting of the adaptive filter stage 78.

In some examples, the adaptive front-end controller 68 is synchronizedwith the RF tuner 72 by sharing a master clock signal communicatedbetween the two. For example, cognitive radios operating on a 100 μsresponse time can be synchronized such that for every clock cycle theadaptive front end analyzes the input RF signal, determines an optimalconfiguration for the adaptive filter stage 78, filters that RF signalinto the filtered RF signal and communicates the same to the tuner 72for cognitive analysis at the radio. By way of example, cellular phoneshave may be implemented with a 200 μs response time on filtering. Byimplementing the adaptive front end controller 68 using a fieldprogrammable gate array configuration for the filter stage, wirelessdevices may identify not only stationary interference, but alsonon-stationary interference, of arbitrary bandwidths on that movinginterferer.

In some examples, the controller 80 is connected directly with the tuner72 to provide data from the signal processing algorithms directly to theradio tuner such as optimized channel specific SNR data, which the tunermay use to better identify available frequency bands within the filteredRF signal.

In some implementations, the adaptive front-end controller 68 may filterinterference or noise from the received incoming RF signal and pass thatfiltered RF signal to the tuner 72. In other examples, such as cascadedconfigurations in which there are multiple adaptive filter stages, theadaptive front-end controller 68 may be configured to apply the filteredsignal to an adaptive bandpass filter stage to create a passband portionof the filtered RF signal. For example, the tuner 72 may communicateinformation to the controller 68 to instruct the controller that theradio is only looking at a portion of an overall RF spectrum and thus toinstruct the adaptive front-end controller 68 not filter only certainportions of the RF spectrum and bandpass only those portions. Theintegration between the tuner 72 and the adaptive front-end controller68 may be particularly useful in dual-band and tri-band applications inwhich the tuner 72 is able to communicate over different wirelessstandards, such as both GSM or UMTS standards.

The algorithms that may be executed by the controller 80 are not limitedto particular ones, although primarily interference detection andelimination is desired for most radio applications. In any event, by wayof example, in some configurations the controller 80 may execute aspectral blind source separation algorithm that looks to isolate twosources from their convolved mixtures. The controller 80 may execute asignal to interference noise ratio (SINR) output estimator for all orportions of the RF signal. The controller 80 may perform bidirectionaltransceiver data link operations for collaborative retuning of theadaptive filter stage 78 in response to instructions from the tuner 72or from data the transmitter stage 64. The controller 80 will of coursedetermine the optimal filter tuning coefficient data for configuring thevarious adaptive filters of stage 78 to properly filter the RF signal.The controller 80 may also include a data interface communicating thetuning coefficient data to the tuner 72.

In the illustrated embodiment the filtered RF signal may be convertedfrom a digital signal to an analog signal within the adaptive front-endcontroller 68. This allows the controller 68 to integrate in a similarmanner to conventional RF filters. In other examples, a digitalinterface may be used to connect the adaptive front-end controller 68with the tuner 72, in which case the ADC 70 would be removed.

The above discussion is in the context of the receiver stage 62. Similarelements are show in the transmitter stage 64, but bearing a prime. Theelements in the transmitter stage 64 may be identical to those of thereceiver 62, as shown, or different. Furthermore, some or all of theseelements may in fact be executed by the same corresponding structure inthe receiver stage 62. For example, the RF receiver tuner 72 and thetransmitter tuner 72′ may be the performed by the same single tunerdevice. The same may be true for the other elements, such as theadaptive filter stages 78 and 78′, which may both be implemented in asingle FPGA, with different filter elements in parallel for full duplex(simultaneous) receive and transmit operation.

The adaptive front-end controller configuration may be used in numerousapplications, such as a cognitive radio system. FIG. 4A illustrates anexample RF signal within which a cognitive radio is to communicate. Afrequency spectrum 90 contains frequency regions that are currently inuse, meaning that remote devices and base stations are communicating onfrequency channels in those regions. One region is between 50 MHz and200 MHz and another is between 450 MHz and 600 MHz. In contrast, thefrequency region between 200 MHz and 450 MHz is not in use, and thus isavailable for communication by the cognitive radio. Under normaloperations, the cognitive radio may scan the RF signal 90 and identifythe entire frequency region between 200 MHz and 450 MHz as beingavailable. However, turning to FIG. 4B, an RF signal 92, similar to theRF signal 90, contains RF characteristics 94, which in this case areinterference frequency signals 94, within that available region. Thesignals 94 may be stationary or may hop around to different frequencieswithin that region. As RF characteristics, the signals 94 may representnoise or any other type of interference, including data carryinginterfering signals from other wireless devices, data broadcastaccording to other wireless standards not currently in use a device,etc. Furthermore, these interference frequencies may be of similar orcompletely different bandwidths.

Ordinarily, the cognitive radio would seek to avoid the entire frequencyregion from 200 MHz to 450 MHz because of these interferes. However, byusing an adaptive front-end controller such as discussed herein, the RFsignal 92 may be filtered to remove the interference signals 94 (thusforming a filtered RF signal that looks like signal 90) before thecognitive radio begins to analyze for available frequency bands.

The spectral ranges provided in FIGS. 4A and 4B are by way of example.In many applications, adaptive front end controllers may be called uponto scan from the few MHz range to 3 GHz and higher.

These techniques may be used in either single wireless communicationsystems or multiple band devices where multiple standards are supported.For example, some wireless communication devices known as dual- ortri-band and are capable of communicating over numerous standards, suchas over GSM and UMTS depending on the available network. In someinstances, operators even support and thus broadcast over multiplesimultaneous systems, like GSM and UMTS. In such instances, interferencemay be self generated, that is, the network will inherently createinterfering signals, which means that a wireless device communicatingunder one standard may experience data communicated simultaneously underthe other standard as interference. In such instances, the techniquesherein may be used to intelligently identify those interference signals(although they will contain data but from another wireless standard) andwill either adaptively filter or pass those signals as not beinginterference signals and let the wireless device receive data from bothstandards. Furthermore, by having adaptive filters that may beprogrammed on the fly, as discussed in examples herein, a remote devicecan be adapted to identify and filter such interference irrespective ofits current wireless standard, that is, as the device moves to adifferent coverage area and a different wireless standard, the sameadaptive filters may be reconfigured to match the standards of thatarea. Of course, such configuration may be performed initially upondesign as well, by having different banks of filter stages each designedfor a different of the multiple supported standards.

FIG. 4C is a representation of an example RF spectrum in whichheterogeneous networks are made to coexist in the same spectral range.This may occur when two different service providers have networks of thesame communication standard, such as two different GSM networks, thatcover the same area. In this case, the service providers are allocateddifferent portions of the RF spectrum, for example portion 95 for oneprovider and portion 97 for another. Alternatively, the representationof FIG. 4C may reflect completely different heterogeneous networks, forexample, where the portion 95 contains GSM signals in the coverage areaand portion 97 contains UMTS signals. In either case, typically suchspectral regions must be spaced far apart to avoid interference atborder regions. However, with an adaptive filter stage device asdiscussed herein, the spectral regions can be expanded as shown in 95′and 97′ because the adaptive filters can be programmed to intelligentlyfilter interference signals at the borders of these regions so that whena device is operating under one band but receiving signals from both,the non-used any data in the non-used band adjacent the used band may beproperly filtered. An example implementation is that the techniquesherein will be able to prevent a 200 kHz GSM (2G) signal frominterfering with a UMTS (3G) signal by using time-adaptive bandstopfilters.

While this filtering is described using the term interference, it isnoted that any RF characteristic may be selectively filtered or passed.For example, in such heterogeneous network situations the adaptivefilter stages can be tuned to bandpass desired signals and bandstopinterference signals.

In some examples, the wireless devices may identify the wirelessstandard themselves or be pre-programmed for particular standards. Whilein other cases, the wireless devices may determine the applicablestandard based on header information in the received incoming RF signal,which may then identify to the wireless device the shaping or otherconfiguration parameters for setting the adaptive filters. Such headerinformation may already be applied for synchronizing the wireless devicewith the adaptive filter stage to the incoming RF signal. More broadly,the adaptive digital filter stage may be designed to any identifyinformation in an RF signal that indicates the some characteristic ofthat RF signal, from which an adaptive front-end controller may use thatinformation to generate tuning coefficients for the adaptive filters.That identification may occur solely at the front end controller or byhaving the front end controller coordinate with a receiver or otherdevice coupled thereto for analysis of the identifying information.

FIG. 5 illustrates an example implementation of an adaptive front-endcontroller 100, e.g., as may be used for the controller 20 of FIG. 1 orcontroller 60 of FIG. 3. Input RF signals are received at an antenna(not shown) and coupled to an initial analog filter 104, such as lownoise amplifier (LNA) block, then digitally converted via an analog todigital converter (ADC) 106, prior to the digitized input RF signalbeing coupled to a field programmable gate array (FPGA) 108. Theadaptive filter stage described above may be implemented within the FPGA108, which has been programmed to contain a plurality of adaptive filterelements tunable to different operating frequencies and frequency bands,and at least some being adaptive from a bandpass to a bandstopconfiguration or vice versa, as desired. Although an FPGA isillustrated, it will be readily understood that other architectures suchas an application specific integrated circuit (ASIC) or a digital signalprocessor (DSP) may also be used to implement a digital filterarchitecture described in greater detail below.

A DSP 110 is coupled to the FPGA 108 and executes signal processingalgorithms that may include a spectral blind source separationalgorithm, a signal to interference noise ratio (SINR) output estimator,bidirectional transceiver data link operations for collaborativeretuning of the adaptive filter stage in response to instructions fromthe tuner, and optimal filter tuning coefficients algorithm.

FPGA 108 is also coupled to a PCI target 112 that interfaces the FPGA108 and a PCI bus 114 for communicating data externally. A system clock118 provides a clock input to the FPGA 108 and DSP 110, therebysynchronizing the components. The system clock 118 may be locally set onthe adaptive front-end controller, while in other examples the systemclaim 118 may reflect an external master clock, such as that of a radiotuner, when used in a cognitive radio application. The FPGA 108, DSP110, and PCI target 112, designated collectively as signal processingmodule 116, will be described in greater detail below.

In the illustrated example, the adaptive front-end controller 100includes a microcontroller 120 coupled to the PCI bus 114 and anoperations, alarms and metrics (OA&M) processor 122. Although they areshown and described herein as separate devices that execute separatesoftware instructions, those having ordinary skill in the art willreadily appreciate that the functionality of the microcontroller 120 andthe OA&M processor 122 may be merged into a single processing device.The microcontroller 120 and the OA&M processor 122 are coupled toexternal memories 124 and 126, respectively. The microcontroller 120 mayinclude the ability to communicate with peripheral devices; and, assuch, the microcontroller 120 may be coupled to a USB port, an Ethernetport, or an RS232 port, among others (though none shown). In operation,the microcontroller 120 may store lists of channels having interferersor a list of known typically available frequency spectrum bands, as wellas various other parameters. Such a list may be transferred to thereporting and control facility or a base station, via the OA&M processor122, and may be used for system diagnostic purposes.

Diagnostic purposes may include, but are not limited to, controlling theadaptive front-end controller 100 to obtain particular informationrelating to an interferer and retasking the interferer. For example, thereporting and control facility may use the adaptive front-end controller100 to determine the identity of an interferer, such as a mobile unit,by intercepting the electronic serial number (ESN) of the mobile unit,which is sent when the mobile unit transmits information on thenarrowband channel. Knowing the identity of the interferer, thereporting and control facility may contact infrastructure that iscommunicating with the mobile unit (e.g., the base station) and mayrequest the infrastructure to change the transmit frequency for themobile unit (i.e., the frequency of the narrowband channel on which themobile unit is transmitting) or may request the infrastructure to dropcommunications with the interfering mobile unit altogether.

Additionally, in a cellular configuration (e.g., a system based on aconfiguration like that of FIG. 1) diagnostic purposes may include usingthe adaptive front-end controller 100 to determine a telephone numberthat the mobile unit is attempting to contact and, optionally handlingthe call. For example, the reporting and control facility may use theadaptive front-end controller 100 to determine that the user of themobile unit was dialing 911, or any other emergency number, and may,therefore, decide that the adaptive front-end controller 100 should beused to handle the emergency call by routing the output of the adaptivefront-end controller 100 to a telephone network.

The FPGA 108 provides a digital output coupled to a digital to analogconverter (DAC) 128 that converts the digital signal to an analog signalwhich may be provided to a filter 130 to generate a clean RF output tobe broadcast from the base station or mobile station. The digital outputat the FPGA 108, as described, may be one of many possible outputs. Forexample, the FPGA 108 may be configured to output signals based on apredefined protocol such as a Gigabit Ethernet output, an open basestation architecture initiative (OBSAI) protocol, or a common publicradio interface (CPRI) protocol, among others.

FIG. 6 illustrates further details of an example implementation of thesignal processing module 116, it being understood that otherarchitectures may be used to implement a signal detection algorithm. Adecoder 150 receives an input from the ADC 106 and decodes the incomingdata into a format suitable to be processed by the signal processingmodule 116. A digital down converter 152, such as a polyphase decimator,down converts the decoded signal from the decoder 150. The decodedsignal is separated during the digital down conversion stage into acomplex representation of the input signal, that is, into I and Qcomponents which are then are fed into a tunable infinite impulseresponse (IIR)/finite impulse response (FIR) filter 154. The IIR/FIRfilter 154 may be implemented as multiple cascaded or parallel IIR andFIR filters. For example, the IIR/FIR filter 154 may be used withmultiple filters in series, such as initial adaptive bandpass filterfollowed by adaptive bandstop filter. For example, the bandpass filtersmay be implemented as FIR filters, while the bandstop filters may beimplemented as IIR filters. In an embodiment, fifteen cascaded tunableIIR/FIR filters are used to optimize the bit width of each filter. Ofcourse other digital down converters and filters such as cascadedintegrator-comb (CIC) filters may be used, to name a few. By usingcomplex filtering techniques, such as the technique described herein,the sampling rate is lowered thereby increasing (e.g., doubling) thebandwidth that the filter 154 can handle. In addition, using complexarithmetic also provides the signal processing module 116 the ability toperform higher orders of filtering with greater accuracy.

In any case, the I and Q components from the digital down converter 152are provided to the DSP 110 which implements a detection algorithm andin response provides the tunable IIR/FIR filter 154 with tuningcoefficient data that tunes the IIR and/or FIR filters 154 to specificnotch and/or bandpass frequencies, respectively, and specificbandwidths. The tuning coefficient data, for example, may include afrequency and a bandwidth coefficient pair for each of the adaptivenotch filters, that instructs that corresponding filter what frequencyis to be tuned for bandpass or bandstop operation and the bandwidth tobe applied for that operation. In reference to FIGS. 3, 4A, and 4B forexample, in implementing a cognitive radio, the tuning coefficient datacorresponding to a bandpass center frequency and bandwidth may begenerated by the detection algorithm and passed to a tunable FIR filterwithin the IIR/FIR filter 154. The filter 154 may then pass all signalslocated within a passband of the given transmission frequency. Tuningcoefficient data corresponding to a notch filter may be generated by thedetection algorithm and then applied to an IIR filter within the IIR/FIRfilter 154 to remove any narrowband interference located within thepassband of the bandpass filter. The tuning coefficient data generatedby the detection algorithm are implemented by the tunable IIR/FIRfilters 154 using mathematical techniques known in the art. Forinstance, the transfer function of a bandpass filter may be given by:

${H(s)} = \frac{H_{o}\beta \; s}{s^{2} + {\beta \; s} + \omega_{o}^{2}}$

where ω_(o) is the center frequency, β is the bandwidth and H_(o) is themaximum amplitude of the filter. In the case of a cognitive radio, uponimplementation of the detection algorithm, the DSP 110 may determine andreturn coefficients corresponding to a specific frequency and bandwidthto be implemented by the tunable IR/FIR filter 154 through a DSP/PCIinterface 158. Similarly, the transfer function of a notch (or bandstop)filter may also be implemented by the tunable IIR/FIR filter 154. Ofcourse other mathematical equations may be used to tune the IIR/FIRfilters 154 to specific notch or bandpass frequencies and to a specificbandwidth.

After the I and Q components are filtered to the appropriate notch orbandpass frequency and specific bandwidth, a digital upconverter 156,such as a polyphase interpolator, converts the signal back to theoriginal data rate; and the output of the digital upconverter isprovided to the DAC 128.

A wireless communication device capable to be operated as a dual- ortri-band device communicating over multiple standards, such as over GSMand UMTS may use the adaptive digital filter architecture as describedabove. For example, a dual-band device (using both UMTS and GSM) may bepreprogrammed within the DSP 10 to transmit first on UMTS, if available,and on GSM only when outside of a UMTS network. In such a case, theIIR/FIR filter 154 may receive tuning coefficient data from the DSP 110to pass all signals within a UMTS range. That is, the tuning coefficientdata may correspond to a bandpass center frequency and bandwidth adaptedto pass only signals within the UMTS range. The signals corresponding toa GSM signal may be filtered, and any interference caused by the GSMsignal may be filtered using tuning coefficients, received from the DSP110, corresponding to a notch frequency and bandwidth associated withthe GSM interference signal.

Alternatively, in some cases it may be desirable to keep the GSM signalin case the UMTS signal fades quickly and the wireless communicationdevice may need to switch communication standards rapidly. In such acase, the GSM signal may be separated from the UMTS signal, and bothpassed by the adaptive front-end controller. Using the adaptive digitalfilter, two outputs may be realized, one output corresponding to theUMTS signal and one output corresponding to a GSM signal. The DSP 110maybe programmed to again recognize the multiple standard service andmay generate tuning coefficients corresponding to realize a filter, suchas a notch filter, to separate the UMTS signal from the GSM signal. Insuch examples, an FPGA may be programmed to have parallel adaptivefilter stages, one for each communication band.

To implement the adaptive filter stages, in some examples, the signalprocessing module 116 is pre-programmed with general filter architecturecode at the time of production, for example, with parameters definingvarious filter types and operation. The adaptive filter stages may thenbe programmed, through a user interface or other means, by the serviceproviders, device manufactures, etc. to form the actual filterarchitecture (parallel filter stages, cascaded filter stages, etc.) forthe particular device and for the particular network(s) under which thedevice is to be used. Of course, dynamic flexibility is achieved duringruntime, where the filters may be programmed to different frequenciesand bandwidths, each cycle, as discussed herein.

One method of detecting a wideband signal having narrowband interferenceis by exploiting the noise like characteristic of a signal. Due to suchnoise like characteristics of the signal, a particular measurement of anarrowband channel power gives no predictive power as to what the nextmeasurement of the same measurement channel may be. In other words,consecutive observations of power in a given narrowband channel areun-correlated. As a result, if a given measurement of power in anarrowband channel provides predictive power over subsequentmeasurements of power in that particular channel, thus indicating adeparture from statistics expected of a narrowband channel withoutinterference, such a narrowband channel may be determined to containinterference. A method of determining such a narrowband channel havinginterference is illustrated in the following FIGS. 7 and 8 and issimilar to the techniques described in U.S. application Ser. No.11/217,717, filed Sep. 1, 2005, entitled “Method and Apparatus forDetecting Interference Using Correlation,” which is incorporated hereinby reference in its entirety.

FIG. 7 illustrates a flowchart of an interference detection program 200that may be used by the DSP 110 to determine location of interference ina signal. A block 202 continuously scans for a series of signals andstores the observed values of the signal strengths that correspond toeach of the various narrowband channels located in the signal. Forexample, the block 202 may continuously scan a 1.2288 MHz DSSS signalfor each of forty one narrowband channels dispersed within it.Alternatively, the block 202 may continuously scan a wideband OFDMAsignal for any narrowband signals which may be located within a givenpassband. The block 202 may be implemented by any well known DSPs usedto scan and store signal strengths in a DSSS or OFDMA signal. Thescanned values of narrowband signal strengths may be stored in a memoryof the DSP or in any other computer readable memory. The block 202 maystore the signal strength of a particular narrowband channel along withany information, such as a numeric identifier, identifying the locationof that particular narrowband channel within the signal.

Subsequently, a block 204 determines a number of sequences m of a signalthat may be required to be analyzed to determine narrowband channelshaving interference. A user may provide such a number m based on anypre-determined criteria. For example, a user may provide m to be equalto four, meaning that four consecutive signals need to be analyzed todetermine if any of the narrowband channels within that signal spectrumincludes an interference signal. As one of ordinary skill in the artwould appreciate, the higher the selected value of m, the more accuratewill be the interference detection. However, the higher the number m is,the higher is the delay in determining whether a particular signal hadan interference present in it, subsequently, resulting in a longer delaybefore a filter, such as the tunable IIR/FIR filter 154, is applied tothe signal to remove the interference signal.

Generally, detection of an interference signal may be performed on arolling basis. That is, at any point in time, m previous signals may beused to analyze presence of an interference signal. The earliest of suchm interference signals may be removed from the set of signals used todetermine the presence of an interference signal on a first-in-first-outbasis. However, in an alternate embodiment, an alternate sampling methodfor the set of signals may also be used.

Subsequently, a block 206 selects x narrowband channels having thehighest signal strength from each of the m most recent signals scannedat the block 202. The number x may be determined by a user. For example,if x is selected to be equal to three, the block 206 may select threehighest channels from each of the m most recent signals. The methodologyfor selecting x narrowband channels having highest signal strength froma signal is described in further detail in FIG. 8 below. For example,the block 206 may determine that the first of the m signals hasnarrowband channels 10, 15 and 27 having the highest signal strengths,the second of the m channels has narrowband channels 15 and 27 and 35having the highest signal strengths, and the third of the m channels hasthe narrowband channels 15, 27 and 35 having the highest narrowbandsignal strength.

After having determined the x narrowband channels having the highestsignal strengths in each of the m signals, a block 208 compares these xnarrowband channels to determine if any of these highest strengthnarrowband channels appear more than once in the m signals. In case ofthe example above, the block 208 may determine that the narrowbandchannels 15 and 27 are present among the highest strength narrowbandchannels for each of the last three signals, while channel 35 is presentamong the highest strength narrowband channels for at least two of thelast three signals.

Such consistent appearance of narrowband channels having highest signalstrength over subsequent signals indicate that narrowband channels 15and 27, and probably the narrowband channel 35, may have an interferencesignal super-imposed on them. A block 210 may use such information todetermine which narrowband channels may have interference. For example,based on the number of times a given narrowband channel appears in theselected highest signal strength channels, the block 210 may determinethe confidence level that may be assigned to a conclusion that a givennarrowband channel contains an interference signal.

Alternatively, the block 210 may determine a correlation factor for eachof the various narrowband channels appearing in the x selected highestsignal strength channels and compare the calculated correlation factorswith a threshold correlation factor to determine whether any of the xselected channels has correlated signal strengths. Calculating acorrelation factor based on a series of observations is well known tothose of ordinary skill in the art. For digital signal processing, thecorrelation factors may be determined using autocorrelation, which is amathematical tool for finding repeating patterns, such as the presenceof a periodic signal which has been buried under noise, or identifyingthe missing fundamental frequency in a signal implied by its harmonicfrequencies. Autocorrelation is used frequently in signal processing foranalyzing functions or series of values, such as time domain signals.Informally, correlation determines the similarity between observationsas a function of the time separation between them. More precisely,correlation may be achieved through the cross-correlation of a signalwith itself. The threshold correlation factor may be given by the userof the interference detection program 200.

Note that while in the above illustrated embodiment, the correlationfactors of only the selected highest signal strength channels arecalculated, in an alternate embodiment, correlation factors of all thenarrowband channels within the signals may be calculated and compared tothe threshold correlation factor.

Empirically, it may be shown that when m is selected to be equal tothree, for a clean signal, the likelihood of having at least one matchamong the higher signal strength narrowband channels is 0.198, thelikelihood of having at least two matches among the higher signalstrength narrowband channels is 0.0106, and the likelihood of having atleast three matches among the higher signal strength narrowband channelsis 9.38.times.10^(̂-5). Thus, the higher the number of matches, thelesser is the likelihood of having a determination that one of the xchannels contains an interference signal (i.e., false positiveinterference detection). It may be shown that if the number of scans mis increased to, say four scans, the likelihood of having such matchesin m consecutive scans is even smaller, thus providing higher confidencethat if such matches are found to be present, they indicate presence ofinterference signal in those narrowband channels.

To identify the presence of interference signals with even higher levelof confidence, a block 212 may decide whether to compare the signalstrengths of the narrowband channels determined to have an interferencesignal with a threshold. If the block 212 decides to perform such acomparison, a block 214 may compare the signal strength of each of thenarrowband channels determined to have an interference with a thresholdlevel. Such comparing of the narrowband channel signal strengths with athreshold may provide added confidence regarding the narrowband channelhaving an interference signal so that when a notch filter is positionedat that narrowband channel, the probability of removing anon-interfering signal is reduced. However, a user may determine thatsuch added confidence level is not necessary and thus no such comparisonto a threshold needs to be performed. In which case, the control passesto a block 216, which stores the interference signals in a memory.

After storing the information about the narrowband channels havinginterference signals, a block 218 selects the next signal from thesignals scanned and stored at the block 202. The block 218 may cause thefirst of the m signals to be dropped and the newly added signal is addedto the set of m signals that will be used to determine presence of aninterference signal (first-in-first-out). Subsequently, control ispassed to the block 206 where the process of determining narrowbandchannels having interference signals is repeated. Finally, a block 220may generate tuning coefficient data which may be passed to the IIR/FIRfilters 154 to realize a particular filter structure. In one embodiment,the tuning coefficient data may correspond to specific notchfrequencies. In an alternative embodiment, such as, for example, acognitive radio implementation, the tunable coefficients may correspondto a bandpass frequency and bandwidth. The tuning coefficient data maybe passed to the tunable IIR/FIR filter 154, as shown in FIG. 6 toselect and activate one or more IIR and/or FIR filters that are locatedin the path of the signal to filter out any narrowband channelidentified as having narrowband interference in it.

Now referring to FIG. 8, a flowchart illustrates a high strengthchannels detection program 250 that may be used to identify variouschannels within a given scan of the DSSS signal that may contain aninterference signal. The high strength channels detection program 250may be used to implement the functions performed by the block 206 of theinterference detection program 200. In a manner similar to theinterference detection program 200, the high strength channels detectionprogram 250 may also be implemented using software, hardware, firmwareor any combination thereof.

A block 252 may sort signal strengths of each of the n channels within agiven DSSS signal. For example, if a DSSS signal has forty onenarrowband channels, the block 252 may sort each of the 41 narrowbandchannels according to its signal strengths. Subsequently, a block 254may select the x highest strength channels from the sorted narrowbandchannels and store information identifying the selected x higheststrength channels for further processing. An embodiment of the highstrength channels detection program 250 may simply use the selected xhighest strength channels from each scan of the DSSS signals todetermine any presence of interference in the DSSS signals. However, inan alternate embodiment, additional selected criteria may be used.

Subsequently, a block 256 determines if it is necessary to compare thesignal strengths of the x highest strength narrowband channels to anyother signal strength value, such as a threshold signal strength, etc.,where such a threshold may be determined using the average signalstrength across the DSSS signal. For example, the block 256 may use acriterion such as, for example: “when x is selected to be four, if atleast three out of four of the selected narrowband channels have alsoappeared in previous DSSS signals, no further comparison in necessary.”Another criterion may be, for example: “if any of the selectednarrowband channels is located at the fringe of the DSSS signal, thesignal strengths of such narrowband channels should be compared to athreshold signal strength.” Other alternate criteria may also beprovided.

If the block 256 determines that no further comparison of the signalstrengths of the selected x narrowband channels is necessary, control ispassed to a block 258, which stores information about the selected xnarrowband channels in a memory for further processing. If the block 256determines that it is necessary to apply further selection criteria tothe selected x narrowband channels, control is passed to a block 260.The block 260 may determine a threshold value against which the signalstrengths of each of the x narrowband channels are compared based on apredetermined methodology.

For example, in an embodiment, the block 260 may determine the thresholdbased on the average signal strength of the DSSS signal. The thresholdsignal strength may be the average signal strength of the DSSS signal ora predetermined value may be added to such average DSSS signal to derivethe threshold signal strength.

Subsequently, a block 262 may compare the signal strengths of theselected x narrowband channels to the threshold value determined by theblock 260. Only the narrowband channels having signal strengths higherthan the selected threshold are used in determining presence ofinterference in the DSSS signal. Finally, a block 264 may storeinformation about the selected x narrowband channels having signalstrengths higher than the selected threshold in a memory. As discussedabove, the interference detection program 200 may use such informationabout the selected narrowband channels to determine the presence ofinterference signal in the DSSS signal.

The interference detection program 200 and the high strength channeldetection program 250 may be implemented by using software, hardware,firmware or any combination thereof. For example, such programs may bestored on a memory of a computer that is used to control activation anddeactivation of one or more notch filters. Alternatively, such programsmay be implemented using a digital signal processor (DSP) whichdetermines the presence and location of interference channels in adynamic fashion and activates/de-activates one or more notch filters.

Accordingly, this description is to be construed as illustrative onlyand not as limiting to the scope of the invention. The details of themethodology may be varied substantially without departing from thespirit of the invention, and the exclusive use of all modifications,which are within the scope of the appended claims, is reserved.

What is claimed is:
 1. A method comprising: identifying, by a systemincluding a processor, a spectral region in a radio frequency spectrum;determining, by the system, a signal strength of the spectral region;determining, by the system, a correlation factor by correlating thesignal strength of the spectral region; detecting, by the systemaccording to the correlation factor, interference in the spectralregion; generating, by the system, coefficient data that corresponds toat least one of notch frequencies, a bandpass frequency or a bandwidth;and configuring, by the system, a filter according to the coefficientdata to substantially suppress the interference in the spectral regionand to produce a digital filtered signal, wherein the configuring of thefilter includes activating at least one target filter of a group oftarget filters, and wherein the group of target filters includes atleast one infinite impulse response filter or finite impulse responsefilter.
 2. The method of claim 1, comprising selecting the spectralregion for engaging in a communication session independent of therebeing interference in the spectral region.
 3. The method of claim 1,comprising transmitting, by the system, the digital filtered signal to abase station.
 4. The method of claim 1, comprising formatting, by thesystem, the digital filtered signal according to a Gigabit Ethernetprotocol.
 5. The method of claim 1, comprising formatting, by thesystem, the digital filtered signal according to an Open Base StationArchitecture Initiative (OBSAI) protocol.
 6. The method of claim 1,comprising formatting, by the system, the digital filtered signalaccording to a Common Public Radio Interface (CPRI) protocol.
 7. Themethod of claim 1, wherein the system is located in a base station.
 8. Amethod, comprising: selecting, by a system including a processor, anavailable frequency band for engaging in communications independent ofthere being interference in the available frequency band; determining,by the system, a signal strength for each of a plurality of narrowbandchannels in the available frequency band; determining, by the system, acorrelation factor for at least a portion of the plurality of narrowbandchannels based on correlating of the signal strength; identifying, bythe system from the correlation factor, narrowband interference in anarrowband channel of the plurality of narrowband channels; generating,by the system, tuning coefficient data based on the narrowbandinterference; and configuring, by the system, a filter according to thetuning coefficient data to substantially suppress the narrowbandinterference in the narrowband channel and to produce a digital filteredsignal.
 9. The method of claim 8, comprising transmitting, by thesystem, the digital filtered signal to a base station.
 10. The method ofclaim 9, wherein the system is located in a mobile device that iscommunicatively coupled to the base station.
 11. The method of claim 8,wherein the system is located in a cellular base station.
 12. The methodof claim 8, comprising formatting, by the system, the digital filteredsignal for transmission according to one of a Gigabit Ethernet protocol,an Open Base Station Architecture Initiative (OBSAI) protocol or aCommon Public Radio Interface (CPRI) protocol.
 13. The method of claim8, wherein the configuring of the filter comprises tuning the filter toa filter frequency and a filter bandwidth according to the tuningcoefficient data, wherein the filter comprises a plurality of filterelements that are arranged in parallel or in a cascaded configuration.14. The method of claim 8, wherein the determining of the correlationfactor comprises repeating a determination of the signal strength foreach of the plurality of narrowband channels in the available frequencyband.
 15. A system, comprising: a filter apparatus; and a circuitcoupled to the filter apparatus, wherein execution of instructions bythe circuit causes the circuit to perform operations comprising:selecting portions of a spectral region for engaging in a communicationsession independent of there being interference in the portions of thespectral region; determining a signal strength for a plurality ofchannels corresponding to the portions of the spectral region;determining a correlation factor by a correlation of the signalstrength; identifying from the correlation factor a channel of theplurality of channels experiencing interference; generating coefficientdata based on the interference; and configuring the filter apparatus bytuning the filter apparatus to a filter frequency and a filter bandwidthaccording to the coefficient data to substantially suppress theinterference and to produce a digital filtered signal.
 16. The system ofclaim 15, wherein the determining of the correlation factor comprisesdetermining correlation factors for a group of higher signal strengthswithout determining correlation factors for a group of lower signalstrengths.
 17. The system of claim 15, wherein the filter apparatuscomprises a plurality of filter elements.
 18. The system of claim 17,wherein the plurality of filter elements are arranged in a parallelconfiguration.
 19. The system of claim 17, wherein the plurality offilter elements are arranged in a cascaded configuration.
 20. The systemof claim 15, wherein the system is located in a mobile device that iscommunicatively coupled to a cellular base station, and wherein theoperations further comprise transmitting the digital filtered signal tothe cellular base station.